The pair prediction formulation allows the model to explicitly capture the term-level matches between \(Q\)  and \(D\) , as its self-attention mechanism of BERT is applied on all pairs of query and document terms. The dual-encoder model, however, has to compress all information of \(Q\)  or \(D\)  to two dense vectors, respectively, and performs query-document matching at the document (or query) level, which is coarser than the term-level matching. [1]}, [2]} report that the ranking models based on term-level matching are more effective than the models based on document-level matching for IR.
